There are several approaches to improving the ACs and thus possibly increasing the accuracy of the P1AC result on challenging images. Improving the AC quality might also lead to a speed up in sample consensus because the initial pose estimates by the P1AC solver would be more accurate, and thus the system would spend less time during the local optimization steps. Photometric refinement of the affine transformation is commonly used [1]} but is computationally expensive [2]}. Barath et al. [3]} an efficient alternative called least squares matching (LSM) for symmetric affine correspondence refinement. Eichhardt and Barath introduced a method for multi-view correction of affine frames which could be used to improve the affine frames in the training images but not the query images, since it requires knowledge of the camera pose beforehand [4]}.
Alternatively, Mischkin et al. introduced a deep learning framework for rapidly detecting high-quality affine regions [5]}.
